Semi-Automatic Segmentation of Fibrous Liver Tissue

Abstract

This article presents a semi-automatic segmentation of the fibrous liver tissue in the in-vivo liver biopsy color images. The segmentation is performed using a tree-based classifier, with decision rules as tree leaves and binary operators (AND, OR) as tree nodes. Several image's local characteristics have been exploited, based on the image points' intensity levels, as well as taken from the texture analysis domain (fractal dimension, FFT, Gabor filters). Their effectiveness concerning quality of extraction has been compared using real clinical images with a manual delimitation given by physicians, as a reference. A user friendly application has been developed which enables the operator to interactively create and store the classifiers. It also offers to a physician a predefined set of the best found classifiers, to allow him an effective work in his every-day practice. The method is semi-automatic - it still leaves to the operator, beside the classifier choice, a possibility to manually (with the mouse) adjust the main parameter (s) which visually, on the fly, grows/shrinks the extracted fibrous region.